{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/29444"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/29444","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Dynamic Retail Location Model with Market Learning","abstract":"In this thesis, we study the optimal store location decisions for a firm entering a new market where the market adoption rate can be learned over time. In the presence of market learning, the firm faces a trade-off between active learning and deferred commitment. To illustrate this trade-off , we introduce a two-stage retail location problem in which the market learning time (length of the first stage) is endogenously determined by the firm's first stage action. To solve the problem, we develop an efficient solution method which provides a framework to achieve a desired error rate of accuracy in the optimal solution. The proposed algorithm is tested on the network constructed using census data from the city of Chicago. Using the model, we first show that the lack of foresight results in lower profit with over-commitment in facility investment and that the difference increases with market uncertainty. We further show that the firm should prefer active learning over deferred commitment as consumers in the market become more conservative in making product adoption decisions.","abstract_html":"In this thesis, we study the optimal store location decisions for a firm entering a new market where the market adoption rate can be learned over time. In the presence of market learning, the firm faces a trade-off between active learning and deferred commitment. To illustrate this trade-off , we introduce a two-stage retail location problem in which the market learning time (length of the first stage) is endogenously determined by the firm's first stage action. To solve the problem, we develop an efficient solution method which provides a framework to achieve a desired error rate of accuracy in the optimal solution. The proposed algorithm is tested on the network constructed using census data from the city of Chicago. Using the model, we first show that the lack of foresight results in lower profit with over-commitment in facility investment and that the difference increases with market uncertainty. We further show that the firm should prefer active learning over deferred commitment as consumers in the market become more conservative in making product adoption decisions.","abstract_has_math":false,"creators":["Bhatti, Shahzad F."],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Industrial Engineering","degree_department":null,"school":null,"contributors":["Chen, Xin","Lim, Michael K."],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2012,"date_issued":"2012-02-01T00:46:41Z","date_published":"2012-02-01T00:46:41Z","updated_at":"2026-07-22T22:25:27Z","subjects":["Facility location","Maximal covering problem","Retail operations","Market learning"],"languages":["en"],"rights":["Copyright 2011 Shahzad F. 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